• Journal of Korean Society of Water and Wastewater
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• v.12 no.3
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• pp.99-106
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• 1998

This paper presents a procedure for determining the design rainfall depth and the design rainfall intensity at Incheon city area in Korea. In this study the eight probability distributions are considered to estimate the probable rainfall depths for 11 different durations. The Kolmogorov - Smirnov test and the Chi-square test are adopted to test each distribution. The probable rainfall intensity formulas are then determined by i) the least squares (LS) method, ii) the least median squares (LMS) method, iii) the reweighted least squares method based on the LMS (RLS), and iv) the constrained regression (CR) model. The Talbot, the Sherman, the Japanese, and the Unified type are considered to determine the best type for the Incheon station. The root mean squared (RMS) errors are computed to test the formulas derived by four methods. It is found that the Unified type is the most reliable and that all methods presented herein are acceptable for determining the coefficients of rainfall intensity formulas from an engineering point of view.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.159-167
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• 2003

This study suggests the hybrid fault diagnosis method of signed digraph (SDG) and partial least squares (PLS). SDG offers a simple and graphical representation for the causal relationships between process variables. The proposed method is based on SDG to utilize the advantage that the model building needs less information than other methods and can be performed automatically. PLS model is built on local cause-effect relationships of each variable in SDG. In addition to the current values of cause variables, the past values of cause and effect variables are inputted to PLS model to represent the Process armies. The measured value and predicted one by dynamic PLS are compared to diagnose the fault. The diagnosis example of CSTR shows the proposed method improves diagnosis resolution and facilitates diagnosis of masked multiple-fault.

• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.1
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• pp.72-75
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• 2018

An initial rotor position estimation method is proposed in this study for an interior permanent-magnet synchronous motor without a resolver or an absolute encoder. This method uses least squares approximation to estimate the initial rotor position. The magnetic polarity is identified by injection of short pulses. The proposed estimation process is robust because it does not require complex signal processing that depends on the performance of a digital filter. In addition, it can be applied to various servo systems because it does not require additional hardware. Experimental results validate the effectiveness of the proposed method using a standard industrial servomotor with interior-permanent magnets.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.135-145
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• 1995

A new algorithm was given to successively fit the multiexponential function/nonlinear function to data by a weighted least squares method, using Gauss-Newton, Marquardt, gradient and DUD methods for convergence. This study also considers the problem of linear-nonlimear weighted least squares estimation which is based upon the usual Taylor's formula process.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.319-322
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• 1992

This paper proposes indirect adaptive pole placement adaptive controller using a modified least squares method. If an adaptive controller has good performance, it is necessary that an estimator have fast convergence. This paper presents a modified least squares method which guarantees the stability of estimator and has fast convergence. In this algorithm, information on signal level is obtained from the determinent of covariance matrix and according to it, weighting factor is tuned.

• Journal of the Chungcheong Mathematical Society
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• v.22 no.2
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• pp.177-186
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• 2009

The global least squares method (Gl-LSQR) is a generalization of LSQR method for solving linear system with multiple right hand sides. In this paper, we present how to apply this algorithm for solving the image restoration problem and illustrate the usefulness and effectiveness of this method from numerical experiments.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.621-628
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• 2018

More than four visible GPS (Global Positioning System) satellites are required to obtain absolute positioning. However, it is not easy to satisfy this condition when a rover is in such unfavorable condition as an urban area. As a consequence, clock-aided positioning has been used as an alternative method especially when the number of visible satellites is three providing that receive clock error information is available. In this study, LSC (Least-Squares Collocation) method is proposed to interpolate clock errors for clock-aided positioning after analyzing the characteristics of receiver clock errors. Numerical tests are performed by using GPS data collected at one of Korean CORS (Continuously Operating Reference Station) and a nearby GPS station. The receiver clock errors are obtained through the DGPS (Differential GPS) positioning technique and segmentation procedures are applied for efficient interpolation. Then, LSC is applied to predicted clock error at epoch which clock information is not available. The numerical test results are analyzed by examining the differences between the original and interpolated clock errors. The mean and standard deviation of the residuals are 0.24m and 0.49m, respectively. Therefore, it can be concluded that sufficient accuracy can be obtained by using the proposed method in this study.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.150-158
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• 2013

In this paper we present approximated constrained least squares filter for real-time directionally adaptive image restoration. The proposed method makes a hardware implementation easier for real-time image restoration because of reducing the filter size. Furthermore, for directional adaptive image restoration, this paper estimates the local orientation by analyzing the covariance matrix and applies to approximated constrained least squares filter. Experimental results show that the proposed method is sharper and less artifacts than existing methods.

• Geophysics and Geophysical Exploration
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• v.8 no.2
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• pp.170-176
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• 2005

Recently the velocity stack inversion is having many attentions as an useful way to perform various seismic data processing. In order to be used in various seismic data processing, the inversion method used should have properties such as robustness to noise and parsimony of the velocity stack result. The IRLS (Iteratively Reweighted Least-Squares) method that minimizes ${L_1}-norm$ is the one used mostly. This paper introduce another method, CGG (Conjugate Guided Gradient) method, which can be used to achieve the same goal as the IRLS method does. The CGG method is a modified CG (Conjugate Gradient) method that minimizes ${L_1}-norm$. This paper explains the CGG method and compares the result of it with the one of IRSL methods. Testing on synthetic and real data demonstrates that CGG method can be used as an inversion method f3r minimizing various residual/model norms like IRLS methods.

• Journal of the Korean Statistical Society
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• v.16 no.2
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• pp.80-91
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• 1987

Minimum $L_i$ norm estimation is a robust procedure ins the sense that it leads to an estimator which has greater statistical eficiency than the least squares estimator in the presence of outliers. And the $L_1$ norm estimator has some desirable statistical properties. In this paper a new computational procedure for $L_1$ norm estimation is proposed which combines the idea of reweighted least squares method and the linear programming approach. A modification of the projective transformation method is employed to solve the linear programming problem instead of the simplex method. It is proved that the proposed algorithm terminates in a finite number of iterations.